Faculty Publications
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Item Transfer Learning Based Model for Colon Cancer Prediction Using VGG16(Institute of Electrical and Electronics Engineers Inc., 2023) Koppad, S.; Annappa, B.; Acharjee, A.Colon cancer, or a colorectal cancer, is a malignant neoplasm that originates in the colon. It is one of the most prevalent forms of cancer globally, with significant impacts on morbidity and mortality rates. The essential task is to detect it and detect it at an initial phase for curing the patient precisely. The artificial intelligence plays important roles in the colon cancer prediction. The authors proposed various models on colon cancer prediction using ML and DL. The existing approaches are unable to achieve good accuracy for the colon cancer prediction. This research work suggests a transfer learning based framework for the colon cancer prediction. This framework is planned on the basis of VGG16 and CNN in colon cancer prediction. The proposed framework is implemented in python and results is analysed concerning accuracy, precision, recall. © 2023 IEEE.Item Detection of Pneumonia from Chest X-Ray Images(Institute of Electrical and Electronics Engineers Inc., 2024) Shetty, S.P.; Mamatha, N.; Shetty, M.; Keerthana, S.; Shetty D, P.Pneumonia is a dangerous which is caused by various viral agents. The diagnosis and treatment of pneumonia can be difficult because of the similarities with other lung diseases, which underscores the importance of chest x-rays for an early detection. This work presents techniques of pneumonia detection implementing CNNs, VGG16 and ResNet152V2 architectures, together with the Gradient Descent optimization method. The model is trained and tested on one of Kaggle's dataset which have 5,836 images that are labeled. This system automatically extract features from the chest X-Ray images and uses Gradient Descent optimization to improve its ability to differentiate between the pneumonia patients and healthy cases. For given dataset, the result provides accuracy of 96.56%, 95.34%, 92.9% and 94.23% for RestNet152V2,CNN,VGG16 and Gradient Descent respectively. Therefore this framework will facilitate to the detection of lung disease for experts and doctors as well. © 2024 IEEE.Item Diabetic Retinopathy Detection Using Novel Loss Function in Deep Learning(Springer Science and Business Media Deutschland GmbH, 2024) Singh, S.; Annappa, B.; Dodia, S.Globally, the number of diabetics has significantly increased in recent years. Several age groups are affected. Diabetic Retinopathy (DR) affects those with diabetes for a long time. DR is a side effect of diabetes that affects the retina’s blood vessels and is caused by high blood sugar levels. Therefore, early detection and treatment are preferred. Manual recognition concerns and a lack of technology support for ophthalmologists are the most complex problems. Nowadays, Deep Learning (DL) based approaches are used significantly for creating DR detection systems because of the ongoing development of Artificial Intelligence (AI) techniques. This paper uses the APTOS dataset of retina images to train four deep Convolution Neural Network (CNN) models using a novel loss function. The four DL models used are VGG16, Resnet50, DenseNet121, and DenseNet169 to explain their rich properties and improve the classification for different phases of DR. The experimental results of this study demonstrate that VGG16 produced the lowest accuracy of 73.26% on the APTOS dataset, while DenseNet169-based detection gives the most significant result of 96.68% accuracy among the four approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Item Real-time microscopy image-based segmentation and classification models for cancer cell detection(Springer, 2023) Devi, T.G.; Patil, N.; Rai, S.; Philipose, C.P.Image processing techniques and algorithms are extensively used for biomedical applications. Convolution Neural Network (CNN) is gaining popularity in fields such as the analysis of complex documents and images, which qualifies the approach to be used in biomedical applications. The key drawback of the CNN application is that it can’t call the previous layer output following the layer’s input. To address this issue, the present research has proposed the novel Modified U-Net architecture with ELU Activation Framework (MU-EAF) to detect and classify cancerous cells in the blood smear images. The system is trained with 880 samples, of which 220 samples were utilized in the validation model, and 31 images were utilized to verify the proposed model. The identified mask output of the segmentation model in the predicted mask fits the classification model to identify the cancer cell occurrence in the collected images. In addition, the segmentation evaluation is done by matching each pixel of the ground truth mask (labels) to the predicted labels from the model. The performance metrics for evaluating the segmentation of images are pixel accuracy, dice coefficient (F1-score), and Jaccard coefficient. Moreover, the model is compared with VGG-16 and simple modified CNN models, which have four blocks, each consisting of a convolutional layer, batch normalization, and activation layer with RELU activation function that are implemented and for assessing the same images used for the proposed model. The proposed model shows higher accuracy in comparison. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.